Showing papers by "Roel F. Veerkamp published in 2009"

Effects of the number of markers per haplotype and clustering of haplotypes on the accuracy of QTL mapping and prediction of genomic breeding values.

Abstract: The aim of this paper was to compare the effect of haplotype definition on the precision of QTL-mapping and on the accuracy of predicted genomic breeding values. In a multiple QTL model using identity-by-descent (IBD) probabilities between haplotypes, various haplotype definitions were tested i.e. including 2, 6, 12 or 20 marker alleles and clustering base haplotypes related with an IBD probability of > 0.55, 0.75 or 0.95. Simulated data contained 1100 animals with known genotypes and phenotypes and 1000 animals with known genotypes and unknown phenotypes. Genomes comprising 3 Morgan were simulated and contained 74 polymorphic QTL and 383 polymorphic SNP markers with an average r2 value of 0.14 between adjacent markers. The total number of haplotypes decreased up to 50% when the window size was increased from two to 20 markers and decreased by at least 50% when haplotypes related with an IBD probability of > 0.55 instead of > 0.95 were clustered. An intermediate window size led to more precise QTL mapping. Window size and clustering had a limited effect on the accuracy of predicted total breeding values, ranging from 0.79 to 0.81. Our conclusion is that different optimal window sizes should be used in QTL-mapping versus genome-wide breeding value prediction.

Estimation of genetic variation in residual variance in female and male broiler chickens

Abstract: In breeding programs, robustness of animals and uniformity of end product can be improved by exploiting genetic variation in residual variance. Residual variance can be defined as environmental variance after accounting for all identifiable effects. The aims of this study were to estimate genetic variance in residual variance of body weight, and to estimate genetic correlations between body weight itself and its residual variance and between female and male residual variance for broilers. The data sets comprised 26 972 female and 24 407 male body weight records. Variance components were estimated with ASREML. Estimates of the heritability of residual variance were in the range 0.029 (s.e. = 0.003) to 0.047 (s.e. = 0.004). The genetic coefficients of variation were high, between 0.35 and 0.57. Heritabilities were higher in females than in males. Accounting for heterogeneous residual variance increased the heritabilities for body weight as well. Genetic correlations between body weight and its residual variance were -0.41 (s.e. = 0.032) and -0.45 (s.e. = 0.040), respectively, in females and males. The genetic correlation between female and male residual variance was 0.11 (s.e. = 0.089), indicating that female and male residual variance are different traits. Results indicate good opportunities to simultaneously increase the mean and improve uniformity of body weight of broilers by selection.

Estimation of prediction error variances via Monte Carlo sampling methods using different formulations of the prediction error variance

Abstract: Calculation of the exact prediction error variance covariance matrix is often computationally too demanding, which limits its application in REML algorithms, the calculation of accuracies of estimated breeding values and the control of variance of response to selection. Alternatively Monte Carlo sampling can be used to calculate approximations of the prediction error variance, which converge to the true values if enough samples are used. However, in practical situations the number of samples, which are computationally feasible, is limited. The objective of this study was to compare the convergence rate of different formulations of the prediction error variance calculated using Monte Carlo sampling. Four of these formulations were published, four were corresponding alternative versions, and two were derived as part of this study. The different formulations had different convergence rates and these were shown to depend on the number of samples and on the level of prediction error variance. Four formulations were competitive and these made use of information on either the variance of the estimated breeding value and on the variance of the true breeding value minus the estimated breeding value or on the covariance between the true and estimated breeding values.

Gene expression patterns in anterior pituitary associated with quantitative measure of oestrous behaviour in dairy cows

Abstract: Intensive selection for high milk yield in dairy cows has raised production levels substantially but at the cost of reduced fertility, which manifests in different ways including reduced expression of oestrous behaviour. The genomic regulation of oestrous behaviour in bovines remains largely unknown. Here, we aimed to identify and study those genes that were associated with oestrous behaviour among genes expressed in the bovine anterior pituitary either at the start of oestrous cycle or at the mid-cycle (around day 12 of cycle), or regardless of the phase of cycle. Oestrous behaviour was recorded in each of 28 primiparous cows from 30 days in milk onwards till the day of their sacrifice (between 77 and 139 days in milk) and quantified as heat scores. An average heat score value was calculated for each cow from heat scores observed during consecutive oestrous cycles excluding the cycle on the day of sacrifice. A microarray experiment was designed to measure gene expression in the anterior pituitary of these cows, 14 of which were sacrificed at the start of oestrous cycle (day 0) and 14 around day 12 of cycle (day 12). Gene expression was modelled as a function of the orthogonally transformed average heat score values using a Bayesian hierarchical mixed model on data from day 0 cows alone (analysis 1), day 12 cows alone (analysis 2) and the combined data from day 0 and day 12 cows (analysis 3). Genes whose expression patterns showed significant linear or non-linear relationships with average heat scores were identified in all three analyses (177, 142 and 118 genes, respectively). Gene ontology terms enriched among genes identified in analysis 1 revealed processes associated with expression of oestrous behaviour whereas the terms enriched among genes identified in analysis 2 and 3 were general processes which may facilitate proper expression of oestrous behaviour at the subsequent oestrus. Studying these genes will help to improve our understanding of the genomic regulation of oestrous behaviour, ultimately leading to better management strategies and tools to improve or monitor reproductive performance in bovines.

Estimating genomic breeding values from the QTL-MAS Workshop Data using a single SNP and haplotype/IBD approach

Abstract: Genomic breeding values were estimated using a Gibbs sampler that avoided the use of the Metropolis-Hastings step as implemented in the BayesB model of Meuwissen et al., Genetics 2001, 157:1819–1829.

Estimating reliabilities of genomic breeding values

Abstract: The availability of genomic estimated breeding values (GEBV) allows the selection of young bulls with relatively high reliability before phenotypic performance of daughters is recorded. Reported absolute gains in reliability for those young bulls, compared to using pedigree indexes only, are up to 20% (Hayes et al., 2009). In the countries where GEBV are currently used in the national evaluation, GEBV are calculated using the usual procedures, but replacing a pedigree based relationship matrix by a genomic relationship matrix (GRM) (Berry et al., 2009, VanRaden, 2008). Subsequently, reliabilities of GEBV are obtained by inverting the left-hand sides of the mixed model equations (Berry et al., 2009, VanRaden, 2008). An alternative method to obtain reliabilities is cross-validation (e.g. De Roos et al., 2009). Since the availability of correct reliabilities of GEBV is important both for national and international genetic evaluations, the objective of this paper was to provide a method to validate reliabilities of GEBV.

Statistical genetics to improve robustness of dairy cows

Abstract: Rapid changes in farm scale and pressures on costs and farm management together with the negative correlation between production and fitness traits have stimulated the demand for robust cows. We have defined a robust cow as: “a cow that is able to maintain homeostasis in the commonly accepted and sustainable dairy herds of the near future” which clearly contains an element of environmental sensitivity and genotype by environment interaction (GxE). Classical breeding solutions to breed for more robustness are i) avoiding inbreeding, ii) multi-trait selection and iii) allowing more natural selection. Statistical models allow more direct selection for robustness by estimating genetic correlations between environments (discrete macro scale), reaction norms describing genotypes as a function of a continuous environmental parameter (continuous macro scale) and genetic variation in residual variance which is environmental sensitivity for a large number of unidentifiable environmental aspects with a relatively small effect each (unknown micro scale). The application of these complex models is still under development, but first results indicate more GxE for fitness traits than yield traits, albeit evidence for strong reranking of animals is still limited, so it is mainly the magnitude of the variance which is affected by environment. These statistical models will contribute, together with classical tools and new phenotypic and genomic measurement tools, to the breeding of cows that also fit in future dairy systems.

Economic indices for various breeds under different farming systems and price uncertainty - Case Slovenia

Abstract: Marija Klopcic1, Roel Veerkamp2, Silvo Žgur1, Jože Osterc1, Milena Kovac1, Spela Malovrh1, Stane Kavcic1, Marko Cepon1, Pat Dillon3, Laurence Shalloo3, Abele Kuipers and Yvette de Haas2 Biotechnical Faculty, University of Ljubljana, Domzale, Slovenia, Wageningen UR Livestock Research, Lelystad, The Netherlands Teagasc, Moorepark, Ireland Expertise Centre for Farm Management and Knowledge Transfer, WageningenUR, The Netherlands

Breeding for a global dairy market using genomic selection

Abstract: From the simulation of a closed nucleus breeding program for dairy cattle it was concluded that the introduction of genomic selection and the use of young animals as parents increased the rate of genetic gain by a factor 2.4 when genetic markers explained 50% of the genetic variance. In this situation, all bulls in the top 100 EBV list were young bulls. While genomic selection reduced the rate of inbreeding, the actual rate of inbreeding per year was increased by a factor 1.6 because of the use of young animals as parents. When a reference population was available in environment A but not in environment B, selection based on the average EBV in environment A and B was the most effective strategy when the genetic correlation between A and B was t 0.90. When the genetic correlation between A and B was d 0.75 the rate of genetic gain was lower across all strategies. Splitting the population gave the highest rate of genetic gain but also the highest rate of inbreeding.